Nurses’ Experiences of Grieving When There Is a Perinatal Death
Bibliographic record
Abstract
Many nurses grieve when patients die; however, nurses’ grief is not often acknowledged or discussed. Also, little attention is given to preparing nurses for this experience in schools of nursing and in orientations to health care organizations. The purpose of this research was to explore obstetrical and neonatal nurses’ experiences of grieving when caring for families who experience loss after perinatal death. A visual arts-informed research method through the medium of digital video was used, informed by human science nursing, grief concepts, and interpretive phenomenology. Five obstetrical nurses and one neonatal intensive care nurse who cared for bereaved families voluntarily participated in this study. Nurses shared their experiences of grieving during in-depth interviews that were professionally audio- and videotaped. Data were analyzed using an iterative process of analysis-synthesis to identify themes and patterns that were then used to guide the editing of the documentary. Thematic patterns identified throughout the data were growth and transformation amid the anguish of grief, professional and personal impact, and giving–receiving meaningful help. The thematic pattern of giving–receiving meaningful help was made up of three thematic threads: support from colleagues; providing authentic, compassionate, quality care; and education and mentorship. Nurses’ grief is significant. Nurses who grieve require acknowledgment, support, and education. Supporting staff through their grief may ultimately have a positive impact on quality of work life and home life for nurses and quality of care for bereaved families.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".